I've been thinking a lot about borders recently for no particular reason and borders are a very innately human thing if I don't have the right piece of paper I cannot cross this line in the sand like it's a very very real problem that I face and many many people face every single day and borders are just one kind of constraint that humans just make up and I think that's very interesting that we respect borders so much but AI does not AI is a border disrespector it is very very easily multilingual so if you trained an LLM on mostly mostly English text corpus it's going to learn other languages just as a side effect it's going to be very natively multimodal because it can you can turn llama into a vision language model with just like 100 bucks of just post training there's it's very disrespecting of ground truth borders because it can just doesn't know the difference between hallucination and memorizing from a world model and it also doesn't respect copyright which is a whole other topic that we won't get into today but it's also super fascinating and how does that relate to do with AI engineering right like I think a lot of you here are here because you are interested in that concept at least maybe you identify as an AI engineer maybe you're trying to hire an AI engineer so there are a lot of definitions floating around and I confess that you know I've contributed to that is engineering an API line right that's the that's the line a lot of people have and that's come under some debate recently and yeah that's one form of AI engineering and I think that is useful to some people for understanding like where the responsibilities in a team might stop in and start with the other people in the team or maybe it's there's different subtypes right like last AI engineer summit I talked about the three types of AI engineer that I was seeing emerge the AI enhanced engineer the AI products engineer and the non-human agentic AI engineer or it could be a job description that you try to sort of list out and this is something that on the latent space podcast we recently went through with illicit talking about the different roles that they see within their teams as well so okay if I broadly have any of these three things do I have I nailed down a good definition of AI engineer that is workable yes right but is that something that we're happy with this is something that we can is there nothing left to explore I think the answer is no I think there's more to explore I think the very easy cop-out as well for people discussing this is that you have your opinion I have my opinion you come from your point of view I come from my point of view we agree to disagree or we agree that you know the different different strokes different folks and we move on I don't really like that just because there's no shared agreement on the things that is ground truth to everybody so I want to raise that challenge a little bit more I was in a podcast with Fraza Habib who's one of the speakers today talking about what this conference is and why this conference is does what it does and I always say that AI engineer conferences are effectively my highest stakes expression of what I think the state of AI engineering is so this time last year 2023 AI years are two times of human years we had a few tracks and we had a few topics that were up for debate we had RAG code gen and then agents and multi-modality all those tracks are repeated here today I have some speakers illustrated here just for illustrative purposes these obviously are not every not everyone involved but I think just like the inside-out metaphor that I've been thinking a lot about as the engineer matures so there's the number of concerns that you have to juggle in your head so this year you know after you're a competent AI engineer this year you're now faced with like okay I have to migrate to open models I have to build up my evals maybe I should have done that first that's a whole topic of discussion maybe I should scale up my inference or maybe I should deploy it to the fortune 500 and maybe on the management side of things I should be hiring teams of AI engineers and managing AI strategy for my company I think the last track you know like I talked about the nine tracks in in the engineers it's always about the network the community the network that we're building that's probably the single most important part of this conference and that's the part that we cannot sell we cannot I cannot put on the website hey we have good community because no one will believe us you have to come and see for yourself but please for those of you who've been uploading to the google photos album we've been tweeting out your photos and sharing them on LinkedIn please keep doing that that's a way for myself and everyone else who is not at the conference to try to join in on the fun the reason I'm not comfortable with any of these tracks because I is because I know how they were made because I made them up and I know that because I was looking at the the original document for the rise of the AI engineer it's we're celebrating the one year anniversary today and just down in the document somewhere I just listed out the you know disciplines that I thought the engineer would have and those eventually became mostly mapping to the tracks that I have been exploring in these conferences and the meetups that I do and it's arbitrary like why is there a separate agents track from code gen why is there a separate rag track from open models like these are all related what you know they're all of a kind and obviously as a competent engineer you should be familiar with all these things and that brings us back to this mindset of having boundaries and borders right these are all made up by someone I made them up for this one but you know you're going to live in a world where your boss made it up at your company and these are not reflective of how reality actually has to operate right if you start with all the rules and a different group of people came in would they agree on the same rules probably not just because they're made up but the laws of nature are hard to make up because the reality actually works that way if you had an alien civilization come down to earth they would discover that gravity works the same the sun the energy from the sun works the same the magnetic field works the same so what are the laws of AI engineering we'll come we'll come to that via definitions of software engineering and real engineering so I went and looked up definitions of software engineering and IEEE talks about the application of engineering to software with the design implementation testing and documentation of software Google developers talks about mostly the same thing they also mentioned software life cycle management okay like really reasonable definitions of kind until you look at real engineers real engineers talk about applying natural science sciences utilizing them for benefit of humanity both the IEEE and the national association of engineers agree on that and it's curiously missing from software engineering like the benefiting humanity part the understanding natural laws part completely missing from software engineering so my proposal to you is that AI engineering is somewhere in between right the the the the software engineering but then encountering a lot of the more of the real world constraints than you would in a typical software engineer career so if we know the laws of earth and they are independently derived they cannot make no matter what point of view you're looking at they all are the same laws then what are the equivalent laws of AI engineering I have a few you can come up with more but I'm just going to propose some to start off the base there's constants so for example if you're designing for humans you should respect the fact that humans only speak at 80 words per minute but they read at 200 words per minute right so there's an inherent disparity there there's also constant contingent facts that things that are true for now instead of true forever and true for now facts are for example like the apple intelligence when they ship a local model on every phone then that inference speed of 30 tokens per second that they're advertising becomes the baseline speed limit a speed barrier of what intelligence that is too cheap to meter should look like and these things they're not set in stone they're not actual physical laws so they also trend over time due to forces and momentum and I want to establish a little bit like I think it's under it's very beneficial for AI engineers to understand what the Moore's laws of AI is so that you can plan for them so that you don't have to make the bad bets that are not going to last just obviously just because of overwhelming evidence the first bet is the improving of context right a year ago I was interviewing mosaic and talking about mpc7b with their whopping 60 000 70 000 token context with a lot of loss today sitting in the audience we have people who have trained million token context windows and we've also uh have from anthropic which just released cloud three five years last week um the the fact that you know that we have complete utilization it's not just about the length of context it's also about the utilization of context and I've been Greg who's sitting in the audience as well would be very happy with how Claude is improving on their utilization of their very very long context windows there's also the cost of intelligence the commodification of intelligence so in the past two years we've seen a 99.55 percent decline in the cost of GPT-3 level intelligence the cost of GPT-4 level intelligence has probably come down maybe 90 percent maybe maybe 80 percent 80 percent um from from GPT-4 to Lama 3 and now all the the newer models that are finally I think it's worth commenting a little bit on where AI engineering stands in contrast to other AI philosophies there's EA versus EAC and maybe we're in the middle like we we care about safety but we also want to accelerate right it's kind of like a weird combination of the two things my proposal is that that one dimension isn't enough to express how AI engineer differs from the other philosophies and actually need to add a second dimension to talk about utility we are utility maxis above all else we see what's out there and we want to use it to benefit humanity so my message to everyone at the world's fair is to try to disrespect borders a little bit try to avoid your own dogmatic beliefs lazy consensus of other people or passive reactions and in other words try to disagree disagree more disagree with your own conclusions disagree with each other productively and disagree with the status quo and I think there you'll find that this conference becomes more of a useful landmark in your careers rather than just the party which it can very well be but my final analogy which I really like is that AI engineers are the kind of person that looks at shoggoth and sees instead of a monster that cannot be tamed they want to turn them into mass rapid transit and the kind of person that looks at that looks at you know a force of nature and wants to turn it into tools that are useful for people is the kind of engineer that I would love to speak to and welcome at conferences like this one so that's my view of what borders and engineering without borders should look like I very much encourage you to jump between tracks to jump between friend groups to jump between disciplines and modalities because here's the one place that you can do that outside of your work and to mingle with everyone else that we've gathered so I hope you enjoy doing that I wish I was there in person but just share them online and I hope to see you in person at the next one bye